Azure Analytics – Timely insight for Data-driven decisions

Azure Analytics – Timely insight for Data-driven decisions

Reading Time: 4 minutes

A data-driven culture is critical for businesses to thrive in today’s environment. In fact, a brand-new Harvard Business Review Analytic Services survey found that companies who embrace a data-driven culture experience a 4x improvement in revenue performance and better customer satisfaction.

Foundational to this culture is the ability to deliver timely insights to everyone in your organization across all your data. That is exactly what Microsoft aims to deliver with Azure Analytics and Power BI, and we should say that their cloud-first approach and efforts are paying off in value for customers. According to a recent commissioned Forrester Consulting Total Economic Impact™ study, Azure Analytics and Power BI deliver incredible value to customers with a 271 per cent ROI, while increasing satisfaction by 60 per cent.

Azure Analytics’ position in the leaders quadrant in Gartner’s 2019 Magic Quadrant for Analytics & BI, coupled with their performance in analytics could help businesses to have a strong foundation needed to implement a data-driven culture.

Basically, there are three key attributes needed to establish a data-driven culture

First, it is vital to get the best performance from your analytics solution across all your data, at the best possible price.

Second, it is critical that your data is accurate and trusted, with all the security and privacy rigour needed for today’s business environment.

Finally, a data-driven culture necessitates self-service tools that empower everyone in your organization to gain insights from your data.

Let’s take a deeper look into each one of these critical attributes.


When it comes to performance, Azure has it well covered. An independent study by GigaOm found that Azure SQL Data Warehouse is up to 14x faster and costs 94% less than other cloud providers. This unmatched performance is why leading companies like Anheuser-Busch Inbev adopt Azure.

Business can leverage the elasticity of SQL Data Warehouse to scale the instance up or down, so that customer only pays for the resources when they’re in use, significantly lowering our costs. This architecture performs significantly better than the legacy on-premises solutions and it also provides a single source of truth for all of the company’s data.


Azure is the most secure cloud for analytics. This is according to Donald Farmer, a well-respected thought leader in the data industry, who recently stated, “Azure SQL Data Warehouse platform offers by far the most comprehensive set of compliance and security capabilities of any cloud data warehouse provider”. Since then, Microsoft announced Dynamic Data Masking and Data Discovery and Classification to automatically help protect and obfuscate sensitive data on-the-fly to further enhance data security and privacy.


Only when everyone in your organization has access to timely insights can you achieve a truly data-driven culture. Companies drive results when they break down data silos and establish a shared context of their business based on trusted data. Customers that use Azure Analytics and Power BI do exactly that. According to the same Forrester study, customers stated.

“Azure Analytics has helped with a culture change at our company. We are expanding into other areas so that everyone can make informed business decisions.” -Study interviewee
“Power BI was a huge success. We’ve added 25,000 users organically in three years.” – -Study interviewee

Azure Analytics and Power BI together can unlock the performance, security and insights for your entire organization. Its matured technology and tools propositions enable you to develop a data-driven culture needed to thrive. customers like Reckitt Benckiser, choose Azure for their analytics needs.

“Data is most powerful when it’s accessible and understandable. With this Azure solution, our employees can query the data however they want versus being confined to the few rigid queries our previous system required. It’s very easy for them to use Power BI Pro to integrate new data sets to deliver enormous value. When you put BI solutions in the hands of your boots on the ground—your sales force, marketing managers, product managers—it delivers a huge impact to the business.”

Wilmer Peres, Information Services Director, Reckitt Benckiser

When you add it all up, Azure Analytics and Power BI offer strong data analytics capabilities and scalability for growing needs. To learn more about Azure’s insights for all advantage, let’s connect!

How data analytics help hospitals deliver better patient care

How data analytics help hospitals deliver better patient care

Reading Time: 9 minutes

Data is everywhere and using it for the business advantage is for everyone and not limited to specific industries. Be it an airline, logistics, eCommerce or hospital. Airlines are apparently are of course more operation intensive, asset heavy and arguably, have to comply with more regulations than hospitals. However best operators are managing it exceptionally well by far most hospitals at keeping costs low and making healthy operational margins without losing the focus on delivering customer experience and value for money. Spicejet airlines, for example, has aptly identified and acted upon key operational parameters that pivots the operational performance: Reducing idle time for planes and keeping the seats filled more often than the competitors. Same way some of busiest airports, Fedex and alike are making a positive impact through their service delivery through most feasible and affordable ways. They all operate in asset heavy service industries.

Above examples are simple and have analogical relevance to how a hospital operates.

There are multiple steps, processes, variables, standards and compliances throughout the customer journey. For example in airlines case, operational process entails steps right from booking to checking in to onboarding and then on flight services, compliances and regulations and a set of checking out process. Every of these processes encompases further smalle pieces of operations spanning across a customer’s experience journey. All these operations involve people and not just machines.

Hospitals these days are facing the same pressure on optimising operational efficiencies and asset utilization that probably airlines, retail and transportation industries have faced for long. As Spicejet, Flipkart, FedEx have stayed competitive in asset- intensive services industries by streamlining operations and getting the max out of their available resources. Hospitals cannot have a long term competitive edge if they keep spending and investing more on infrastructures as short terms fixes to challenges. They must rethink how they are utlisting their available assets in the best possible way to gain ROIs.

To do this, hospitals must look at their data with different lenses like airline, transportation players do. Decision making methodologies must be driven by facts backed with statistics and not only based on a limited set of traditionally available information & experience. It is like having an “operational air traffic control system” for hospital – a centralised repository of vital data and systems around it that has capability to integrate process and analyse a vast variety, velocity and amount of data to learn and predict outcomes. Increased awareness of the potential of data and insights are pushing many healthcare organisations to streamlining operations by using data analytics technologies and tools to mine and process large quantities of data to deliver recommendations to administrative and clinical end users.

Business intelligence and Predictive analytics is playing a key role in improving planning and execution decisions for important care delivery processes and resource utilization( Space, Machines, Human) as well as improving scheduling of staff, availability of key equipment & maintenance. These can lead to better care delivery with optimised asset utilisation and lower costs. Few examples:

Operating Room Utilisation

Operating room is one of most revenue generating assets to the tune of more than 55% of revenues for hospitals. Allocation of OR assets has direct impact on care quality, patient’s experience and preparation staff’s bandwidth. However scheduling them in the most efficient way has been bottlenecked by traditional approaches practiced by most hospitals that involved phones and emails. These means of scheduling and rescheduling is tedious when it comes keeping all stakeholders informed. Obviously the scheduling process is tedious, slow and prone to human errors. Coursey to advance data analytics techniques exploiting cloud, mobile and predictive analytical models that help visualise predicted availability and suggesting time slots for better distribution of hospital resources – Human, Machine & Time – leading to take best out of key assets – the OR.

Surgeons can block the time they need with a single click on a mobile app and the connected apps in the hospitals makes it real time communication and confirmation of OR schedules/availability. Concerned staff can be well aware of any changes/cancellation/additional bookings in real time making the entire planning and execution efficient towards delivering better patient care and higher OR utilisations. At UCHealth in Colorado, scheduling apps allow patients to get treated faster (surgeons release their unneeded blocks 10% sooner than with manual techniques), surgeons gain better control and access (the median number of blocks released by surgeon per month has increased by 47%), and overall utilization (and revenue) increases. With these tools, UCHealth increased per-OR revenue by 4%, which translates into an additional $15 million in revenue annually.

Patient wait times

Same way scheduling for infusions in a function of math and timelines. Mathematically, there are enormous permutations and combinations to pick an optimal slot (to avoid staff and material allocation challenges) for a given type of infusion procedure. Not to mention patient wait time is something that hospitals must minimize.

NewYork-Presbyterian Hospital optimised scheduling based through predictive analytics and machine learning that processed multiple data points around the infusion process to identify patterns and suggest optimised scheduling, resulting in a 45% drop in patient wait times. Infusion center could better manage last-minute add-ons, late cancellations, and no-shows as well as optimize nurses’ work hours.

Emergency Department

Emergency departments are famous for bottlenecks, whether because patients are waiting for lab results or imaging backed up in queues or because the department is understaffed. Analytics-driven software that can determine the most efficient order of ED activities, dramatically reducing patient wait times. When a new patient needs an X-ray and a blood draw, knowing the most efficient sequence can save patients time and make smarter use of ED resources. Software can now reveal historic holdups (maybe there’s a repeated Wednesday EKG staffing crunch that needs fixing) and show providers in real time each patient’s journey through the department and wait times. This allows providers to eliminate recurring bottlenecks and call for staff or immediately reroute patient traffic to improve efficiency. Emory University Hospital, for example, used predictive analytics to forecast patient demand for each category of lab test by time of day and day of week. In so doing, the provider reduced average patient wait times from one hour to 15 minutes, which reduced ED bottlenecks proportionally.

Faster Decisions – ED to inpatient-bed transfer

Predictive tools can also allow providers to forecast the likelihood that a patient will need to be admitted, and provide an immediate estimate of which unit or units can accommodate them. With this information, the hospitalist and ED physician can quickly agree on a likely onboarding flow, which can be made visible to everyone across the onboarding chain. This data-driven approach also helps providers prioritize which beds should be cleaned first, which units should accelerate discharge, and which patients should be moved to a discharge lounge. Using a centralized, data-driven patient logistics system, Sharp HealthCare in San Diego reduced its admit order-to-occupy time by more than three hours.

Efficient Discharge planning

To optimize discharge planning, case managers and social workers need to be able to foresee and prevent discharge delays. Electronic health records or other internal systems often gather data on “avoidable discharge delays” — patients who in the last month, quarter, or year were delayed because of insurance verification problems or lack of transportation, destination, or post-discharge care. This data is a gold mine for providers; with the proper analytics tools, within an hour of a patient arriving and completing their paperwork, a provider can predict with fairly high accuracy who among its hundreds of patients is most likely to run into trouble during discharge. By using such tools, case managers and social workers can create a shortlist of high-priority patients whose discharge planning they can start as soon as the patient is admitted. Using discharge analytics software, MedStar Georgetown University Hospital in Washington, DC, for example, increased its daily discharge volume by 21%, reduced length of stay by half a day, and increased morning discharges to 24% of all daily discharges.

Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, analytics tools can lower health care costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place.